Text Classification
Transformers
Safetensors
PyTorch
Portuguese
modernbert
binary-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use tcepi/prog_integridade_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tcepi/prog_integridade_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tcepi/prog_integridade_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tcepi/prog_integridade_model") model = AutoModelForSequenceClassification.from_pretrained("tcepi/prog_integridade_model") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer.json +2 -2
- tokenizer_config.json +1 -1
tokenizer.json
CHANGED
|
@@ -2,13 +2,13 @@
|
|
| 2 |
"version": "1.0",
|
| 3 |
"truncation": {
|
| 4 |
"direction": "Right",
|
| 5 |
-
"max_length":
|
| 6 |
"strategy": "LongestFirst",
|
| 7 |
"stride": 0
|
| 8 |
},
|
| 9 |
"padding": {
|
| 10 |
"strategy": {
|
| 11 |
-
"Fixed":
|
| 12 |
},
|
| 13 |
"direction": "Right",
|
| 14 |
"pad_to_multiple_of": null,
|
|
|
|
| 2 |
"version": "1.0",
|
| 3 |
"truncation": {
|
| 4 |
"direction": "Right",
|
| 5 |
+
"max_length": 1024,
|
| 6 |
"strategy": "LongestFirst",
|
| 7 |
"stride": 0
|
| 8 |
},
|
| 9 |
"padding": {
|
| 10 |
"strategy": {
|
| 11 |
+
"Fixed": 1024
|
| 12 |
},
|
| 13 |
"direction": "Right",
|
| 14 |
"pad_to_multiple_of": null,
|
tokenizer_config.json
CHANGED
|
@@ -4,7 +4,7 @@
|
|
| 4 |
"cls_token": "[CLS]",
|
| 5 |
"is_local": true,
|
| 6 |
"mask_token": "[MASK]",
|
| 7 |
-
"max_length":
|
| 8 |
"model_input_names": [
|
| 9 |
"input_ids",
|
| 10 |
"attention_mask"
|
|
|
|
| 4 |
"cls_token": "[CLS]",
|
| 5 |
"is_local": true,
|
| 6 |
"mask_token": "[MASK]",
|
| 7 |
+
"max_length": 1024,
|
| 8 |
"model_input_names": [
|
| 9 |
"input_ids",
|
| 10 |
"attention_mask"
|